白化
From Ufldl
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We will first describe whitening using our previous 2D example. We will then describe how this can be combined with smoothing, and finally how to combine this with PCA. | We will first describe whitening using our previous 2D example. We will then describe how this can be combined with smoothing, and finally how to combine this with PCA. | ||
| - | How can we make our input features uncorrelated with each other? We had already done this when computing <math> | + | How can we make our input features uncorrelated with each other? We had already done this when computing <math>x_{rot}^{(i)}=U^Tx^{(i)}</math>. Repeating our previous figure, our plot for <math>x_{rot}</math> was: |
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首先我们将通过之前的 2D 例子描述白化。然后描述其与smoothing的结合, 最后讨论如何与PCA结合。 | 首先我们将通过之前的 2D 例子描述白化。然后描述其与smoothing的结合, 最后讨论如何与PCA结合。 | ||
| - | 我们如何消除输入特征之间的相关性? 在计算<math> | + | 我们如何消除输入特征之间的相关性? 在计算<math>x_{rot}^{(i)}=U^Tx^{(i)}</math>时我们其实已经完成了。回顾之前的图表, 在坐标系中绘出<math>x_{rot}</math>: |
:【一校】： | :【一校】： | ||
下面我们先用前文的2D例子描述白化的主要思想，然后分别介绍如何将白化与平滑和PCA相结合。 | 下面我们先用前文的2D例子描述白化的主要思想，然后分别介绍如何将白化与平滑和PCA相结合。 | ||
| - | 在前文计算<math> | + | 在前文计算<math>x_{rot}^{(i)}=U^Tx^{(i)}</math>时我们实际上已经消除了输入特征<math>x^{(i)}</math>之间的相关性。得到的新特征<math>x_{rot}</math>的分布如下图所示： |
